基于RSSM的虚拟传感和无传感器闭环控制,用于多温度区域连续结晶器
Mingrong Dong1,2, Hang Liu1,2, Geng Yang1,2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了一种使用基于模型的强化学习 (MBRL) 的新型虚拟传感器,以精确控制工业结晶器的温度,即使是无法观察到的区域. 使用MBRL方法显著提高温度准确性,降低控制成本.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 精确的温度控制对于连续结晶器的产品质量和能源效率至关重要.
- 工业结晶器由于非线性动力学,强合和传感器限制的不可观察状态而存在挑战.
- 传统的控制策略与部分可观测性和模型不确定性作斗争.
研究的目的:
- 开发基于模型的强化学习 (MBRL) 框架,用于在多区连续结晶器中精确控制温度.
- 用线下历史数据应对无法观察到的状态所带来的挑战,并模拟不确定性.
- 创建一个高保真度的数字双胞胎,作为实时虚拟传感器.
主要方法:
- 一个递归状态空间模型 (RSSM) 被开发为数字双胞胎和虚拟传感器,以推断不可观察的系统状态.
- 设计了一个多目标奖励功能,以平衡跟踪错误,稳定性和控制成本.
- 该MBRL框架仅使用线下历史数据进行培训.
主要成果:
- 虚拟传感器在预测无法观察到的状态方面表现出了特殊的长期稳定性和高准确性.
- 经过训练的MBRL代理显著优于传统的比例整数导数 (PID) 和模型预测控制 (MPC) 控制器.
- 温度跟踪精度提高了67%以上,控制行动成本降低了93%以上.
结论:
- 拟议的带有虚拟传感器的MBRL框架有效地解决了连续结晶器中部分可观测性的问题.
- 虚拟传感能力使得精确的状态估计可用于强大的控制政策优化.
- 该方法提高了系统运行,从而提高了能源效率和产品质量.
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